indEffectTest {track2KBA}

Test site fidelity

Description

indEffectTest tests whether the variance in overlap between space use areas within a group (e.g within individuals) is significant compared to between groups (e.g. between individuals).

Usage

indEffectTest(
  tracks,
  tripID,
  groupVar,
  plot = TRUE,
  method = c("HR", "PHR", "VI", "BA", "UDOI", "HD"),
  conditional = TRUE,
  levelUD = 50,
  scale,
  grid = 500,
  iterations = 1000
)

Arguments

tracks

SpatialPointsDataFrame. Must be projected into an equal-area coordinate system. If not, first run projectTracks.

tripID

character. Column in tracks corresponding to the within group ID (e.g. trip-individual combination)

groupVar

character. Column in tracks corresponding to the between group ID (e.g. individual or track)

plot

logical scalar (TRUE/FALSE). Do you want to output a boxplot of the result?

method

character. Which method of overlap estimation to use? See kerneloverlap for descriptions of each method.

conditional

logical scalar (T/F). If TRUE, the function sets to 0 the pixels of the grid over which the UD is estimated, outside the home range of the animal estimated at a level of probability equal to percent. Note that this argument has no effect when meth="HR" (from kerneloverlap).

levelUD

numeric. The desired contour level of the utilization distribution to be used in overlap estimation. NOTE: this is irrelevant if conditional=FALSE.

scale

numeric (in kilometers). Smoothing ('H') parameter for kernel density estimation.

grid

numeric or SpatialPixels. If numeric, specify the desired number of grid cells over which the utilization distributions will be esimated. A default grid of 500 cells is used.

iterations

numeric. Indicate the desired number of Kolmogorov-Smirnov iterations to run. 500 is an advisable minimum for statistical rigor.

Details

This function works by producing kernel density areas at a desired contour level (i.e. UDLEv) for each level of tripID and estimating the degree of overlap between all pairwise comparisons using the desired overlap method. Then, comparisons are split into 'within' and 'between' groups, determined by the grouping variable (i.e groupVar) argument.

If conditional=TRUE then the overlap estimates will range from 0 to levelUD (unless method="HR").

Then, the empirical distribution of each group is compared in a bootstrapped Kolmogorov-Smirnov test, to check whether differences in the distributions are significant. If so, it indicates that individuals within the groupVar reuse sites more than expected by chance.

NOTE: Because indEffectTest relies on kerneloverlap to estimate overlap, it was not possible to implement a res argument as is done in other track2KBA functions. Therefore, it is advised to either leave the default of 500 cells, or ascertain the number of cells in the grid of chosen res from the output of estSpaceUse.

Value

indEffectTest returns a list containing three objects. In the first slot 'Overlap Matrix', the full matrix of overlap comparisons. In the ' Overlap' slot, a dataframe with a column identifying whether each overlap estimate corresponds to a within-group, or a between-group comparison. In the third slot 'Kolmogorov-Smirnov' is the test output of the Kolmogorov-Smirnov test, indicating the D parameter and significance estimates.

Examples

tracks_raw <- track2KBA::boobies
## format data
tracks_formatted <- formatFields(
  dataGroup = tracks_raw,
  fieldID   = "track_id",
  fieldLat  ="latitude",
  fieldLon  ="longitude",
  fieldDate ="date_gmt",
  fieldTime ="time_gmt"
)

colony <- data.frame(
  Longitude = tracks_formatted$Longitude[1], 
  Latitude = tracks_formatted$Latitude[1]
)
## Split into trips
Trips <- tripSplit(tracks_formatted,
                   colony=colony,
                   innerBuff=2,
                   returnBuff=20,
                   duration=1,
                   nests = FALSE,
                   rmNonTrip = TRUE
)
## project dataset
tracks_prj <- projectTracks(
  Trips,
  projType = "azim",
  custom = "TRUE"
)
## estimate fidelity of individuals across trips
result <- indEffectTest(
  tracks_prj, 
  tripID = "tripID", 
  groupVar = "ID", 
  scale = 30
)